Pass the Microsoft Certified: Security Operations Analyst Associate SC-200 Questions and answers with ExamsMirror

As outlined in Microsoft’s official Defender for Of fice 365 documentation, this service provides comprehensive protection against threats targeting Microsoft 365 collaboration tools—such as SharePoint Online , OneDrive for Business , and Microsoft Teams . The marketing team uses SharePoint Online for vendor c ollabora tion and has experienced incidents in which vendors uploaded malicious files. Microsoft Defender for Office 365 specifically addresses this scenario through features like Safe Attachments and Safe Links , which automatically scan uploaded or shared files for malware and block access to harmful content.

When a vendor uploads a file to SharePoint Online, Defender for Office 365 inspects the file in real time within a virtual sandbox environment before allowing users to open or share it. If malware is d etected, the system quarantines or removes the file and notifies administrators. These detection and remediation capabilities prevent infection propagation, protect sensitive marketing data, and maintain compliance with Contoso’s security posture.

By leveraging Defender for Office 365, Contoso’s marketing team can continue external collaboration safely, ensuring that all uploaded files are scanned and validated before internal access—thereby resolving their specific malware-related issue.

To complete the KQL query against the BehaviorAnalytics t able, you need to know the exact column name (for example, the Boolean field that flags a new or first-time country for the sign-in). Microsoft’s standard method to discover table schemas and column names is the Logs (Log Analytics) query window . In this p ane, the left-hand Schema browser lists all connected tables and, when expanded, shows every column name and data type . Selecting a table (e.g., BehaviorAnalytics ) reveals its fields, and the editor provides IntelliSense/autocomplete for columns as you typ e your KQL, making it straightforward to complete a clause like | where < ColumnName > == true .

Security alerts in Azure Security Center (Defender for Cloud), the Azure Activity log, and Azure Advisor do not expose the per-table column schema needed to build KQL filters. Security Center surfaces alerts and recommendations; the Activity log records control-plane operations; and Advisor provides optimization guidance—none of these replace the Logs experience for exploring data schemas.

Therefore, to accurately identify and verify the column required in the where clause for failed sign-ins from a first-time country, you should use the Log Analytics workspace query window , consult the Schema pane for the BehaviorAnalytics table, and leverage the editor’s autocompl ete to insert the correct column name.

To remediate active attacks automatically once alerts or incidents are detected, Microsoft Sentinel uses playbooks , which are workflows built on Azure Logic Apps . These playbooks can execute remediation actions—such as isolating a machine, blocking an account, or triggering other security control chang es—without manual intervention. Microsoft’s documentation clearly states that “playbooks in Microsoft Sentinel are based on workflows built in Azure Logic Apps” and that they can “automate and orchestrate your threat response by using playbooks … run a pla ybook on-demand or automatically in response to specific alerts or incidents.”

When an analytics rule in Sentinel triggers an alert or incident, you can attach an automation rule which in turn invokes a playbook (i.e. a Logic Apps workflow) to perform the remediation steps. The automation rule defines the trigger conditions and calls the playbook action as part of its response actions.

Let us evaluate other options:

Azure Automation runbooks (Option A) are powerful for scripting in Azure (e.g., PowerShell o r Python) and can perform remediation tasks, but they are not the native mechanism within Sentinel for orchestrated, alert-driven response workflows.

Azure Functions (Option C) are serverless compute for custom code, but you would have to build and integra te orchestration logic manually; they are not the out-of-box SOAR component in Sentinel.

Azure Sentinel livestreams (Option D) is not a recognized remediation automation component—it is irrelevant in this context.

Therefore, the correct solution to remedia te active attacks (triggering automated actions in response to alerts/incidents with minimal manual effort) is to use Azure Logic Apps (via Sentinel playbooks) as the orchestration engine. Logic Apps are the documented foundation of Sentinel’s automation r esponse capabilities.

 Table: DeviceFileEvents

 Aggregation function: count()

In Microsoft Defender XDR advanced hunting, data tables such as DeviceFileEvents , DeviceProcessEvents , and CloudAppEvents are used to investigate various types of activities. Since this query aims to investigate an issue related to file activity—specifically identifying when files have been accessed, modified, or created repeatedly—the correct data source table is DeviceFileEvents . This table contains information about file-level activities recorded by Defender for Endpoint sensors, including file path, file name, action type, and user account involved.

The KQL structure shown in the image follows standard hunting query syntax:

DeviceFileEvents

| where Timestamp > ago(2d)

| summarize activityCount = count() by FolderPath, FileName, ActionType, AccountDisplayName

| where activityCount > 5

Here’s why:

The where Timestamp > ago(2d) clause filters results from the last 2 days, a typi cal timeframe for immediate investigations.

The summarize operator groups events by FolderPath, FileName, ActionType, and AccountDisplayName, then uses count() to determine how many times each file was acted upon.

Finally, where activityCount > 5 filters t o show only unusually high-frequency activity, which might indicate suspicious or automated file manipulation.

Microsoft Defender XDR documentation highlights that DeviceFileEvents is the correct schema for file activity investigations, while DeviceProcess Events focuses on process creation and execution, and CloudAppEvents targets cloud application usage.

Thus, the verified and documented correct completions are:

Table: DeviceFileEvents

Aggregation function: count()

The requirement is to enable Microsoft Defender for Servers Plan 2 on all Azure VMs while excluding Server2 from agentless scanning . Defender for Cloud provides a built-in mechanism to exclude specific machines from agentless scanning based on resource tags . The process is: assign a distinct tag name:value to the VM you want to exclude (Server2), and then, in Defender for Cloud’s Agentless scanning for machines settings, specify that tag pair under exclusions . Defender’s continuous discovery honors these exclusions and skips any VM that matches the configured tag. This approach aligns with the business requirement of least privilege and minimal administrative effort : it avoids broad configuration changes, requires no extensions on the VM, and is reversible by simply removing or changing the tag. The Microsoft Antimalware extension and Automanage configuration are unrelated to agentless scanning behavior, and a resource lock would only prevent modifications/deletions, not scanning. Therefore, to meet the Defender for Cloud requirement precisely, configure an Azure resource tag on Server2 and reference that tag in the agentless scanning exclusion settings.

In Microsoft Sentinel, Hunting queries are used to proactively search for threats and anomalies across collected security data. The requirement states that HuntingQuery1 must run auto matically when the Hunting page of Microsoft Sentinel is accessed. According to Microsoft’s official Sentinel documentation, this behavior is achieved by adding the hunting query to “Favorites.”

When a query is marked as a favorite in the Sentinel Hunting blade, Sentinel automatically runs it every time the Hunting page is opened. This provides updated results without the need for manual execution, ensuring analysts always see the most current data for that query. This configuration also adheres to the orga nization’s requirement to minimize administrative effort , since it eliminates the need for scheduling, automation, or manual refresh actions.

Other options do not meet this functional requirement:

A. Add to a livestream is used to monitor near-real-time da ta streams, not to auto-run queries upon accessing the Hunting page.

B. Create a watchlist is used for referencing static external data sets (like IPs, users, or devices) inside KQL queries, not for automating hunting query execution.

C. Create an automati on rule applies to incident management workflows, not hunting query execution.

Therefore, as per Microsoft Sentinel’s hunting documentation and best practices, the correct and verified answer is:

D. Add HuntingQuery1 to favorites .

For a near-real-time (NRT) analytics rule that detects sign-ins by a designated break-glass account, the most direct and performant pattern is to filter SigninLogs by joining to a Microsoft Sentinel watchlist that contains the protected account(s). Sentinel exposes watchlists to KQL through the helper function _GetWatc hlist( ' < watchlist-name > ' ) , which returns a table with standard columns (including SearchKey ) plus any custom columns you imported. Using join kind=inner ensures the result set includes only those SigninLogs rows whose UserPrincipalName matches an entry in the watchlist—ideal for alerting on a high-value account without post-filtering.

The completed query is:

SigninLogs | join kind=inner (_GetWatchlist( ' breakglass_account ' )) on $left.UserPrincipalName == $right.SearchKey

This approach satisfies the requireme nt to implement an NRT rule for the break-glass account because:

NRT rules support KQL with joins and watchlists and are optimized for rapid evaluation over fresh data.

Using a watchlist lets SecOps adjust monitored accounts without editing the rule—minimi zing administrative effort and aligning with least-privilege operations (no extra permissions beyond watchlist management).

The inner join pattern reduces noise by returning only matched events, which are then turned into alerts/incidents by the NRT rule.

Thus, select join and GetWatchlist , and join UserPrincipalName to the watchlist’s SearchKey .

To monitor and detect higher-than-normal volumes of password resets, you need to gather password reset event data both from Azure Active Directory (cloud identities) and from on-premises Active Directory (domain accounts) . Microsoft’s official Defender XDR and Sentinel integrat ion guidance describes that:

Azure AD Password Protection enforces and monitors password policies in both cloud and hybrid environments. It can detect weak, commonly used, or compromised passwords and logs related password change/reset activities. Deployin g Azure AD Password Protection extends password reset visibility to on-premises domain controllers through the Password Protection proxy and DC agent. This makes it the correct choice for implementing monitoring at the identity environment level.

In Microsoft Sentinel , to ingest and analyze password reset activities from on-premises servers (e.g., domain controllers), you must use the Windows Security Events via AMA connector . This connector collects Event ID 4723 (password change) , 4724 (password res et) , and related security logs directly from Windows Servers into the Sentinel Log Analytics workspace through the Azure Monitor Agent (AMA) . Once the events are available in Sentinel, they can be correlated with other identity or behavioral analytics to d etect abnormal reset volumes or potential compromise attempts.

The other options are not suitable:

Microsoft Defender for Identity focuses on identity compromise detection, not specifically on password reset volume monitoring.

Smart lockout protects agains t brute-force sign-in attempts but doesn’t generate detailed reset event telemetry.

Microsoft security rule and UEBA are higher-level analytic configurations, not data ingestion mechanisms.

Therefore, to meet the Sentinel requirements for monitoring passwo rd reset anomalies:

✅ Implement in the identity environment: Azure AD Password Protection

✅ Configure in Microsoft Sentinel: The Windows Security Events via AMA connector

Microsoft Sentinel playboo ks can be triggered by different types of events— alerts , incidents , or entities . Since the requirement specifies “incidents generated by rulequery1” and asks for automatic processing of those incidents, the correct approach is to use a playbook with an inc ident trigger .

According to Microsoft Sentinel automation documentation:

“When you want automation to start after an incident is created or updated, use the incident trigger . This allows you to automate workflows such as closing incidents, adding comments, or sending notifications.”

This trigger type works with automation rules that specify when and how to execute playbooks based on incident state or severity. Using a playbook with an incident trigger meets the need for post-incident automation (e.g., auto- closing incidents if they match certain conditions).

✅ Answer for Question 8: A. a playbook with an incident trigger

The Sentinel requirement states:

“Ensure that multiple alerts generated by rulequery1 in response to a single user launching Azure Cloud Shell multiple times are consolidated as a single incident.”

This behavior is controlled by the event grouping setting in the analytics rule configurati on.

Microsoft Sentinel documentation explains:

“Event grouping determines how alerts generated from the same rule are grouped into incidents. Selecting ‘Group all alerts triggered by this rule into a single incident’ allows all related alerts to be combine d.”

Hence, configuring event grouping ensures that all alerts from rulequery1 related to the same user are consolidated into one incident, satisfying the requirement.

✅ Answer for Question 10: C. event grouping